Fault diagnosis of the roller bearing using WaveletPacket De-noising and LMD

Sun Wei;Xiong Bangshu;Huang Jianping;Mo Yan

Journal of Vibration and Shock ›› 2012, Vol. 31 ›› Issue (18) : 153-156.

PDF(1316 KB)
PDF(1316 KB)
Journal of Vibration and Shock ›› 2012, Vol. 31 ›› Issue (18) : 153-156.
论文

Fault diagnosis of the roller bearing using WaveletPacket De-noising and LMD

  • Sun Wei1 ,Xiong Bangshu1,Huang Jianping2,Mo Yan1
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Abstract

Local mean decomposition (LMD) method is a new adaptive time-frequency analysis method, which has been successfully applied in the roller bearing fault diagnosis. However, LMD method is sensitive to noise. In order to eliminate noise on the influence of the result of diagnosis, a fault diagnosis approach for the roller bearing based on wavelet packet de-noising and local mean decomposition (LMD) is proposed. Firstly, wavelet packet is used to remove noise from the signal. Then, that result is decomposed by LMD, and the correlation coefficient between the PF and the signal is used as the standard of judgment, so that the redundant low-frequency PF can be rejected. Finally, the effective PF is selected to analyze the power spectrum and extract the fault feature. The experiment of the simulation data and the actual roller bearing fault diagnosis data show that this method is effective.

Key words

rolling bearing / fault diagnosis / LMD / wavelet packet de-noising

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Sun Wei;Xiong Bangshu;Huang Jianping;Mo Yan. Fault diagnosis of the roller bearing using WaveletPacket De-noising and LMD [J]. Journal of Vibration and Shock, 2012, 31(18): 153-156
PDF(1316 KB)

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